The article highlights a major concern emerging from the RSA Conference 2026: while agentic AI—AI systems capable of autonomous decision-making and action—is advancing rapidly, security frameworks are struggling to keep pace. Organizations are deploying AI agents across workflows, cloud systems, and enterprise operations, but the security infrastructure protecting these systems remains immature. This mismatch is creating a widening “AI security gap,” where innovation is outpacing governance and risk management.
A central issue discussed is the lack of visibility and control over AI agents. Unlike traditional software, agentic AI can initiate actions, interact with other systems, and operate independently, making it harder to monitor behavior and detect threats. Many current security tools were designed for static or human-driven systems and cannot fully track AI-driven processes—especially in complex environments like GPU-powered AI infrastructures, where significant blind spots exist.
The article also points to new types of risks introduced by agentic AI. These include unauthorized access, data exposure, and the possibility of AI agents being manipulated or exploited by attackers. Because these systems often operate at high speed and scale, even small vulnerabilities can lead to large-scale consequences. Traditional defenses such as endpoint monitoring or rule-based controls are no longer sufficient, requiring a shift toward AI-native and context-aware security approaches.
Ultimately, the piece concludes that closing the AI security gap will require a fundamental rethink of cybersecurity. Organizations must adopt new architectures, better visibility tools, and stronger governance models specifically designed for autonomous systems. As agentic AI becomes central to enterprise operations, security can no longer be an afterthought—it must evolve alongside AI to ensure trust, resilience, and safe deployment at scale.
